A Fairness-Constrained Multimodal Adaptive Reinforcement Learning Framework for Personalized Candidate Evaluation in Virtual Interviews

Abstract

The surge in the use of artificial intelligence in hiring has created serious issues around algorithmic fairness and absence of bias in candidate assessment, as conventional AI-based interview systems are based on rigid rubrics that tend to reinforce demographic gaps. In order to overcome these issues, we introduce Fair- MARLTrans, a fairness-enforced multimodal adaptive reinforcement learning system for fair candidate assessment through virtual interviews. The model combines visual attributes (face landmarks, gaze, body posture), acoustic attributes (MFCC, prosody, spectral features), and textual attributes (BERT embeddings, sentiment) through a transformer-based attention mechanism to generate a single multimodal representation for end-to-end candidate profiling. Evaluation is framed as a Constrained Markov Decision Process (CMDP) with fairness-sensitive policy gradients imposing demographic parity, equalized opportunity, and individual fairness between protected groups[1]. We propose the Composite Interview Quality Index (CIQI) to simultaneously measure candidate competence and fairness compliance, with theoretical validity guarantees that provide convergence to fair policies. Experiment results on the CMU-MOSI dataset show that Fair-MARLTrans achieves better performance than static rubric baselines (15.14 ± 0.37 mean reward vs. 12.50 ± 0.85 for visual-only), standard reinforcement learning, and fairness-regularized models, with a fairness score of 0.97 and zero constraint violations. Ablation experiments show that multimodal fusion enhances performance by 18.5%, whereas attention mechanisms add 12.3% gains. Demographic sensitivity analysis for ten demographic subgroups holds firmly with fairness compliance (averaged score 0.73, p < 0.01)[2], confirmed by t-tests, ANOVA, and multiple comparison adjustment. This paper provides a theoretical basis for fairness-constrained adaptive assessment and a practical approach to transparent, fair AI-powered recruitment tools, with improved state-of-the-art in algorithmic recruitment.